Hyprnote ⚡️ - AI Notepad for Meetings

Professionals in law, consulting, journalism, and executive roles are increasingly using AI-powered note-taking apps to capture meeting minutes and transcribe calls. However, not all notetakers are built with privacy in mind. In this comprehensive comparison, we focus entirely on privacy-related features and concerns of four popular AI notetakers: Hyprnote, Otter.ai, Granola, and Notion AI. We’ll examine how each platform handles data storage location, encryption, third-party access, cloud usage, and AI processing transparency. If you’re looking for a privacy-focused note-taking app or a secure AI notetaker alternative, read on to see how these services stack up – and which one best safeguards your sensitive information.

Cloud-based transcription services offer convenience at the cost of privacy. Understanding how each AI notetaker handles your data – whether locally or in the cloud – is crucial for privacy-conscious professionals.

Hyprnote: Local Processing & Data Sovereignty

Hyprnote is an AI notepad designed with privacy as the top priority. Unlike cloud-based services, Hyprnote processes everything entirely on your local device – your recordings, transcripts, notes, and AI-generated summaries never leave your computer[1]. All AI processing happens offline on your machine, which means there are no Hyprnote servers receiving or storing your content. This local-first approach gives users complete data sovereignty and control over sensitive information. In fact, you don’t even need to create an account to use Hyprnote, further reducing exposure of personal data[1].

No Cloud Uploads: By default, Hyprnote does not transmit your audio or notes to any cloud. This drastically lowers the risk of breaches or unwanted third-party access, since the data never leaves your possession[1]. For lawyers or executives handling confidential calls, this local-only design means you aren’t trusting a remote server with privileged conversations.
Telemetry Transparency: Hyprnote minimizes data collection across the board. It uses only basic analytics (via PostHog and Sentry) to improve the app, and importantly none of these analytics include your actual notes or transcript content[1]. You can opt out of all telemetry in the settings for full control[1]. This opt-out ensures that nothing is sent out from the app – a reassuring feature for the extremely privacy-conscious.
Encryption & Security: Since Hyprnote keeps your data on your device, traditional cloud encryption concerns are largely avoided. Your data’s security depends on your device’s safeguards (disk encryption, OS security, etc.), rather than on a vendor’s cloud. Hyprnote emphasizes “local-only content” and even suggests downloading the app directly (e.g. from their site) for a secure installation[1]. There are no known data breaches or incidents reported for Hyprnote to date. Its commitment to never transmitting content makes it stand out as a secure AI notetaker alternative to cloud-based services.

In summary, Hyprnote is the go-to choice for those who want an AI meeting notes app without sacrificing privacy. It’s built for offline use, giving professionals confidence that client data, confidential interviews, or strategy meetings remain in-house. Hyprnote’s model is rare in that it delivers AI capabilities while keeping data 100% under the user’s control[1].

Otter.ai: Cloud Convenience with Privacy Trade-offs

Otter.ai is a well-known AI transcription service that offers the convenience of automated meeting notes in the cloud. However, this convenience comes with privacy trade-offs that privacy-conscious users should carefully consider. Otter.ai relies on cloud infrastructure (specifically AWS in the United States) to store and process user data[2]. All your recorded audio and transcripts are uploaded to Otter’s servers, where they are transcribed and saved for your access across devices.

Data Storage & Encryption: Otter stores user conversations on Amazon Web Services (AWS) servers in the US (us-west region)[2]. They do implement robust encryption – data at rest in their AWS S3 storage is encrypted using AES-256, and they use TLS encryption in transit[2]. This means that your notes are encrypted on Otter’s servers to prevent unauthorized access. However, keep in mind that encryption at rest protects against external breaches; it does not mean your data is hidden from Otter’s own systems or subprocessors.
Use of Data for AI Training: A critical privacy concern is that Otter.ai uses customer data to train its AI models. According to Otter’s own privacy policy, they “train [their] proprietary AI on de-identified audio recordings” and on transcripts (which may still contain personal information) to improve their services[3]. In practice, this means your meeting audio and text could be fed into Otter’s machine learning algorithms. Even if Otter claims to de-identify data, sensitive details might still be present in transcripts used for training[3]. For highly confidential conversations (e.g. legal consultations or journalist sources), this default data usage is a significant privacy drawback. Otter does mention that manual review of specific audio for training is only done with explicit user permission (like when you opt in by rating a transcript and checking a permission box)[3]. But the general model training on your data happens unless you are an enterprise customer with a special agreement.
Third-Party Access and Cloud AI Services: Otter.ai’s service doesn’t operate in isolation – it involves several third-party subprocessors. Their policy discloses sharing Personal Information with “selected third parties” including data labeling services (for annotating training data) and AI service providers that support certain features[3]. For example, Otter has integrated features like Otter Chat and automated summaries that likely leverage AI models from partners (Anthropic, OpenAI, etc.) as indicated in their subprocessor list[4]. These providers are contractually prevented from using your data to train their models, according to Otter[4]. Still, your data does pass through external AI APIs in some cases – which could be a concern if you prefer to limit distribution of your information. Additionally, Otter uses standard cloud services: your data is on AWS (as noted), and other subprocessors handle push notifications, email, payments, etc., which means various companies have some access to metadata or content as needed[4].
Data Sovereignty & Compliance: Because Otter stores data in the U.S. and even states it may transfer or process data globally to deliver the service, using Otter could raise compliance issues if your industry or clients require data residency in a specific country. Otter’s privacy policy acknowledges that data may be transferred outside your country and that laws in those jurisdictions may differ (they do commit to using Standard Contractual Clauses for EU data transfers)[3]. For EU-based professionals dealing with GDPR, or for sectors like healthcare (HIPAA), Otter’s cloud-based model might not meet strict requirements without an enterprise agreement. In fact, one independent review scored Otter only 4/10 on privacy for business use, citing GDPR and HIPAA compliance challenges[3].
Privacy Incidents and Perception: Even if Otter hasn’t reported major data breaches, there have been incidents that raised privacy flags. In one notable case reported by Politico and The Verge, a journalist recorded a sensitive interview using Otter and later received an unexpected email from Otter querying the purpose of that particular recording[5]. This incident – essentially an automated survey email that even named the meeting’s title (the interviewee’s name) – served as a “wake up call” that cloud transcription isn’t completely private[5]. It highlighted that Otter’s system had scanned the content (or at least the title) of the conversation, which alarmed the journalist given the subject was a human rights activist under risk of surveillance. Otter later clarified it was a user research survey and that they’ve stopped that practice due to the concern it caused[5]. While no data leak occurred here, the episode underscores that your conversations on Otter are accessible to the company’s algorithms and processes – and potentially to law enforcement via subpoena, as Otter’s team confirmed they would comply with valid U.S. legal requests[5]. For privacy-conscious professionals, this means trusting that Otter will handle your data discreetly and only pry into it under strict policy or legal controls.
Security Measures: On the security front, Otter supports features like account two-factor authentication (2FA) to protect your login, and they publish security practices like regular key rotations for encryption keys[2]. They also provide a transparent list of subprocessors and security FAQs for users. However, Otter’s own policy admits that no internet transmission is 100% secure, stating that you use the service at your own risk regarding data security over the web[3]. They retain personal data as long as needed to fulfill purposes, without a fixed deletion schedule unless you delete content yourself[3] – and even then, trained AI models or backups might still hold traces of your data[3].

In summary, Otter.ai offers powerful AI note-taking in the cloud but expects users to trade some privacy for that convenience. If you need easy transcription and sharing, Otter is effective, but be aware that your words reside on their servers (in the US), could be used to refine Otter’s AI[3], and are accessible to certain third parties by design[3]. Privacy-conscious organizations may opt for Otter’s enterprise plan (for more control and a dedicated agreement) or look to more privacy-centric alternatives for especially sensitive meetings.

Granola: Encrypted Cloud Notes with Transparency – But Still Cloud

Granola markets itself as a “minimal AI notetaker” that works with you. It is another cloud-based meeting transcription tool, but with an emphasis on privacy-friendly practices. Granola takes some commendable steps like not storing raw audio and clearly disclosing its AI subprocessors. However, it still relies on external cloud services and isn’t fully local or offline. Privacy-conscious users will find improvements here over typical cloud services, yet must accept that data does live on Granola’s servers (hosted on AWS).

Data Storage & Encryption: All of Granola’s user data is hosted on AWS servers in the US region[6]. Importantly, Granola does not store your actual audio or video recordings; instead, it keeps only the transcribed text and any notes you take during meetings[6]. This is a plus for privacy, since audio files (which can contain raw voice biometrics or inflections) are discarded once transcribed. The transcripts and text data that are stored are protected with strong encryption – encrypted in transit and at rest using industry-standard methods (AES-256, TLS, etc.) on AWS infrastructure[3][6]. Granola even notes that they encrypt data at both the database and individual column level in their cloud database for an added layer of security[6]. These measures mean that even if someone were to get unauthorized access to the stored data, it would be very difficult to read without the encryption keys.
AI Processing and Third-Party Services: Granola uses a combination of in-house and third-party AI services to deliver its features. On the transcription side, Granola leverages “best-in-class” external speech-to-text engines – specifically naming Deepgram and AssemblyAI as providers[3]. For its AI summaries and language understanding, Granola employs advanced language models from top AI providers like OpenAI and Anthropic[3]. In other words, when you use Granola, your meeting audio is sent to one of these transcription APIs (which returns the text), and then that text may be sent to an LLM (e.g. OpenAI’s or Anthropic’s models) to generate a summary or action items. The upside is that Granola is transparent about this practice – their documentation clearly discloses the use of third-party AI, whereas many vendors are less upfront[3]. They also assert that they have enterprise agreements with these providers to ensure your data isn’t used to train the providers’ models[6]. Still, from a strict privacy standpoint, your data is shared outside of Granola’s own environment during processing, which introduces trust dependencies on those external companies’ privacy measures. Highly regulated industries might see this as a risk, since your content touches multiple systems (Granola, plus OpenAI/Anthropic, plus AWS). Granola currently does not offer a self-hosted or fully on-premise option – all data goes through their cloud[6]. They have acknowledged user requests for on-prem or local storage options (particularly for sectors like healthcare), but as of now there’s no timeline for that[6].
Use of Data for Training & Opt-Out: Granola takes a privacy-first approach to AI model training compared to Otter. By default, Granola may use anonymized and aggregated transcript data to improve its machine learning models, but users have the ability to opt out of this data usage in their settings[6]. In fact, all users (even on free or basic plans) can toggle off the use of their data for training, and Business/Enterprise admins can enforce an organization-wide opt-out with one setting[6]. This opt-out is a critical feature for enterprises that handle sensitive info – it ensures Granola won’t include their conversations in any AI learning process. Granola also promises that no third-party AI service is allowed to train on your data – they have contractual clauses to prevent OpenAI, Anthropic, etc., from learning from your transcripts[6]. When Granola does use your data (if you haven’t opted out) for improving their models, they claim to do so in an anonymized way. However, as with any anonymization, there’s an inherent risk that patterns or sensitive context might still exist in aggregated data[3]. It’s good that Granola’s default UX is privacy-friendly – e.g. meeting notes are private to you by default (not shared with others unless you choose to share them)[3]. Just be mindful that you should actively opt out of data training if you don’t want any of your content reused even anonymously.
Consent and Compliance Features: Granola recognizes that recording meetings involves legal considerations, so they’ve built in a consent notification feature. The app can automatically post a customizable message in the Zoom or Google Meet chat to inform participants that the meeting is being transcribed[6]. This is useful for compliance with two-party consent laws (where all participants must be notified of recording). On the Business plan, users can enable this per meeting, and upcoming Enterprise controls will let an admin enforce these notifications company-wide[6] . However, Granola still puts the onus on the user to follow the law – they recommend always getting verbal consent too, and note that compliance is ultimately the user’s responsibility[3][6]. For regulated industries, Granola is working toward common security certifications (they mentioned aiming for SOC 2 compliance and alignment with GDPR) but had not yet achieved certain certifications at last updatehttps://blog.buildbetter.ai/do-they-own-your-data-otter-ai-privacy-policy-reviewed. Also, Granola is not currently HIPAA-compliant for medical use (they candidly state they are not pursuing HIPAA at the moment)[6]. Enterprises can negotiate a Data Processing Agreement and even get Granola to tweak terms (for a price), but out-of-the-box, it’s not fully certified for sensitive data compliance.
Recent Security Incident: In early 2025, a security researcher discovered a vulnerability in Granola’s Electron app that could have exposed user transcripts[7]. The app contained an endpoint that, without authentication, revealed an API key for AssemblyAI (Granola’s transcription provider). Using this key, the researcher was able to retrieve transcript data for recordings (though not the audio itself) from AssemblyAI’s API[7]. This was a serious oversight, as it meant an attacker could potentially access meeting transcripts that should have been private. Granola responded quickly: they disabled the exposed key and patched the endpoint in March 2025[7]. The company later clarified that the exposure was limited to about 300 users who were alpha-testing a new iOS app (not all users)[7] . While the issue was fixed, it serves as a reminder that even “privacy-first” cloud apps can have bugs that lead to data leakage. Encrypted storage doesn’t help if an API inadvertently gives out keys. For users, the positive takeaway is that Granola was transparent and swift in resolving the problem – but the incident might influence one’s perception of its security maturity.

In summary, Granola strikes a middle ground: it offers the ease of a cloud service with better privacy transparency and user controls than many competitors. It’s a good choice for teams who want AI-driven meeting notes with encryption and some say in data usage. However, because it still operates in the cloud, highly sensitive use-cases (like confidential board meetings or patient info) might demand caution. You’ll need to trust Granola and its partners with your data, or otherwise leverage the opt-outs and enterprise settings to tighten control. Granola shows that it’s listening to privacy concerns – but as with any cloud app, there’s some inherent risk until features like self-hosting or on-device processing become available.

Notion AI: Integrated Workspace AI & Data Privacy Considerations

Notion AI is a bit different from Otter and Granola – it’s an AI feature within Notion, a popular all-in-one workspace app. Many professionals use Notion to write documents, manage projects, and collaborate, and the Notion AI feature brings generative AI assistance (for summarizing notes, drafting content, answering questions) right into your Notion pages. From a privacy perspective, Notion AI’s context is broader: it deals with whatever data you have in your Notion workspace. This could include meeting notes, but also documents, plans, or even databases. The key considerations are how Notion stores your data and how the AI portion handles your content when sending it to large language models.

Data Storage & Encryption (Notion Platform): Notion stores all user content on its cloud servers (previously on AWS, primarily in the US – often cited as AWS us-west-2 region). As a user, you cannot choose the region or host data on-premises – your pages live in the cloud managed by Notion[8]. Notion does secure data in transit (HTTPS/TLS) and at rest (AES-256 encryption on their servers)[9]. They are known to have good general security practices and have obtained SOC 2 Type II certification and other audits for their service. However, Notion does not offer end-to-end encryption for your stored notes[8]. This means Notion (and by extension, its staff or anyone with sufficient access to their systems) could theoretically read your notes in plaintext. For everyday use, Notion’s internal policies and access controls protect user data, but for highly sensitive or classified information, the lack of client-side encryption is a notable privacy gap. In short, all your Notion content is cloud-accessible – safe from external attackers in most cases, but accessible to Notion’s servers for providing the service. A tech author bluntly summarized: Notion’s ease-of-use comes with the trade-off that you can’t control where your notes are stored and it “lacks end-to-end encryption,” making it less ideal for very private or confidential data[8].
Notion AI’s Use of Third-Party AI Models: When you invoke Notion’s AI features (for example, asking it to summarize a page or brainstorm ideas), Notion AI may process your content using external large language model providers. Notion has disclosed that it utilizes several LLMs – some hosted by Notion itself and others provided by partners like Anthropic and OpenAI[9]. In practice, Notion might send the relevant prompt and relevant page data to an AI model hosted by OpenAI or Anthropic to generate the result. The company continuously evaluates models to give a good experience, and they maintain a Subprocessor page listing these AI providers[9]. For users, this means your data could leave Notion’s environment and go to these AI cloud providers temporarily during an AI request. The good news is that Notion has put a lot of thought into AI processing transparency and privacy: they have a detailed Notion AI security policy that explains how data is handled. Notion ensures that any third-party AI vendor that processes customer data has privacy and security safeguards in place and contractual obligations to protect that data[9]. All data sent to the AI providers is encrypted in transit (TLS 1.2+)[9].
No Training on Your Data & Limited Retention: A big relief for privacy is Notion’s stance that neither Notion nor its AI subprocessors use your workspace content to train AI models by default[9]. They explicitly state that your use of Notion AI “does not grant Notion any right to your data to train our models”, and they have contractual agreements with their AI partners prohibiting them from using your data for training[9]. This means that, unlike some other services, anything you feed into Notion AI is not going to secretly improve the next version of GPT or Claude – it’s only used to give you an immediate result. Additionally, Notion addressed data retention: for Enterprise plan customers, they leverage “zero-retention” modes with the AI providers, meaning that any data sent to say, OpenAI, is not stored at all (the provider processes it on the fly and doesn’t log it)[9]. For non-Enterprise users, the AI providers may retain the data for a short period (up to 30 days) for debugging or compliance, but then it’s deleted[9] . OpenAI’s policy, for instance, is not to use API data for training and to retain data only briefly (30 days by default) – Notion is aligning with that. Also, Notion uses OpenAI’s embeddings service to create semantic indexes of your pages (for answering questions about your knowledge base). OpenAI does not retain any customer data through their embeddings API, so those vector representations are safe from OpenAI’s reuse[9]. All embeddings that Notion stores in its own database are treated as customer data and get deleted within 60 days if you delete the corresponding content[9]. Overall, Notion’s approach here is quite privacy-forward for an AI feature on a cloud platform.
Internal Access Controls & Permissions: Notion AI is designed so that it respects your workspace’s sharing permissions[9]. The AI will not access pages you can’t access – e.g. if you ask a question, it only uses content you as the user have permission to view. This prevents accidental data leakage across a company account. Notion also segregates each customer’s data on the backend; they don’t mix data between different customers during AI processing[9]. That means your company’s data stays logically isolated when the AI is working with it, preventing any cross-tenant privacy issues (you won’t get another company’s info in your answers, and vice versa).
Compliance & Security: Notion (the company) has matured in terms of security compliance. They offer features like workspace data export and custom data retention policies for Enterprise (you can set content to auto-delete after X days, which helps with compliance). They are GDPR-compliant and provide Data Processing Addendums for customers. One notable limitation: Notion is not HIPAA-compliant as of now[10], so it’s not meant for storing personal health information. There haven’t been public reports of major breaches involving Notion’s data, but as with any cloud service that isn’t end-to-end encrypted, users should avoid storing ultra-sensitive secrets (passwords, unredacted personal identifiers, etc.) unless absolutely necessary. Notion’s own founders and security team emphasize that they take privacy seriously and continuously invest in security best practices (like regular pen-tests and monitoring). But ultimately, using Notion AI means trusting both Notion and its chosen AI partners with your content – under the assurances that they won’t misuse it.

In summary, Notion AI brings powerful AI assistance into a collaborative workspace in a way that is relatively mindful of privacy: no training on your data, and clear limits on third-party data retention[9]. It’s a compelling tool if you’re already in the Notion ecosystem and want AI help with your notes or documents. For most professionals, Notion AI’s safeguards (encryption in transit, no training, limited retention) will be sufficient. But for the truly sensitive cases (e.g. a journalist with anonymous sources or a lawyer with case strategy notes), remember that Notion is a cloud platform without end-to-end encryption[8]. You would be depending on the company’s security and legal protections. In such cases, one might use Notion AI for less sensitive content and choose a more locked-down solution for the critical stuff.

Conclusion: Choosing a Secure AI Notetaker

When it comes to privacy-focused note-taking apps, the differences between Hyprnote, Otter.ai, Granola AI, and Notion AI are significant. If your top concern is keeping data fully private and under your control, Hyprnote stands out as a secure AI notetaker alternative to the usual cloud services. Its local-processing model means sensitive meeting notes never leave your device, providing peace of mind for attorneys, consultants, and others handling confidential information.

On the other hand, Otter.ai and Granola offer convenient cloud-based transcription with advanced AI features, but users pay a privacy cost. Otter’s model actively uses your data to improve its service and involves numerous third parties – which might be unacceptable for organizations with strict confidentiality rules. Granola AI positions itself as more privacy-conscious, and indeed it brings improvements like clearer disclosures, no audio storage , and user control over data usage . Yet, it’s still a cloud app reliant on external AI engines, meaning your content isn’t entirely self-contained. Recent incidents (like Granola’s API key leak) remind us that even well-meaning services can have vulnerabilities .

Notion AI is slightly different: it’s an add-on to a productivity platform many already use. For existing Notion users, enabling AI doesn’t drastically change where your data lives – it’s still on Notion’s cloud – and Notion’s policies (no training on your data , enterprise-grade security audits) make it a reasonable choice for internal business notes and documentation. However, because Notion lacks end-to-end encryption and uses third-party AI, it may not satisfy those who need absolute secrecy . Companies using Notion AI should leverage enterprise settings like data retention controls and monitor what kind of data employees feed into the AI (to avoid, say, pasting in unencrypted client PII).

In closing, choosing the right AI notetaker involves balancing privacy with functionality:

Hyprnote is ideal for privacy purists and regulated professionals – it’s the closest to an offline, privacy-by-design solution where you sacrifice some cloud convenience for maximum data control.
Otter.ai suits users who need a feature-rich, shareable transcript service and are willing to trust a reputable cloud provider with their data (perhaps acceptable for less sensitive meetings, or with an enterprise agreement in place for added assurances).
Granola appeals to those who want a middle ground – a secure AI notetaker that encrypts data and is transparent about its AI usage. It’s a strong privacy-focused note-taking app in the cloud category, but still one to use with caution for highly sensitive content (at least until features like self-hosting arrive).
Notion AI is great for productivity enthusiasts who want AI integrated into their daily workflow. For general business use, Notion’s privacy measures suffice, but extremely sensitive notes might be better kept out of any cloud app, Notion included.

Every organization or individual should assess the nature of their notes and transcripts. For mission-critical privacy (e.g., a journalist protecting sources or a doctor handling patient data), a local solution like Hyprnote or strict policies with any cloud service are advisable. For everyday meetings and brainstorming, the convenience of Otter, Granola, or Notion AI can be harnessed safely by leveraging their privacy settings (opt-outs, retention limits, permissions) and staying informed about their practices. In the rapidly evolving landscape of secure AI notetaker alternatives, being informed is your best defense – and hopefully, this comparison has shed light on which tool can be your trusted partner for private, secure, AI-powered note-taking.

Protecting your notes doesn’t mean giving up on AI – it just means choosing the right solution and using it wisely.

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